Selecting software reliability growth models and improving their predictive accuracy using historical projects data
Journal article, 2014

During software development two important decisions organizations have to make are: how to allocate testing resources optimally and when the software is ready for release. SRGMs (software reliability growth models) provide empirical basis for evaluating and predicting reliability of software systems. When using SRGMs for the purpose of optimizing testing resource allocation, the model's ability to accurately predict the expected defect inflow profile is useful. For assessing release readiness, the asymptote accuracy is the most important attribute. Although more than hundred models for software reliability have been proposed and evaluated over time, there exists no clear guide on which models should be used for a given software development process or for a given industrial domain. Using defect inflow profiles from large software projects from Ericsson, Volvo Car Corporation and Saab, we evaluate commonly used SRGMs for their ability to provide empirical basis for making these decisions. We also demonstrate that using defect intensity growth rate from earlier projects increases the accuracy of the predictions. Our results show that Logistic and Gompertz models are the most accurate models; we further observe that classifying a given project based on its expected shape of defect inflow help to select the most appropriate model. (C) 2014 Elsevier Inc. All rights reserved.

Software reliability growth models

SYSTEMS

Defect inflow

ERROR-DETECTION

Software Engineering

Computer Science

Embedded software

Computer Science

Author

Rakesh Rana

University of Gothenburg

Miroslaw Staron

University of Gothenburg

Christian Berger

University of Gothenburg

Jörgen Hansson

Chalmers, Computer Science and Engineering (Chalmers), Software Engineering (Chalmers)

M. Nilsson

Volvo

Fredrik Törner

Volvo

Wilhelm Meding

Ericsson Sweden

Christoffer Höglund

Saab AB

Journal of Systems and Software

0164-1212 (ISSN)

Vol. 98 59-78

Subject Categories

Electrical Engineering, Electronic Engineering, Information Engineering

DOI

10.1016/j.jss.2014.08.033